class_docstr: Embedding Frontend for text based inputs.
docstr: Apply a sliding window on the input.
  
  Args:
      input: Input (B, T) or (B, T,D), with D.
      input_lengths: Input lengths within batch.
  
  Returns:
      Tensor: Output with dimensions (B, T, D).
      Tensor: Output lengths within batch.
docstr_tok: ["Apply","a","sliding","window","on","the","input",".","Args",":","input",":","Input","(","B",",","T",")","or","(","B",",","T",",","D",")",",","with","D.","input_lengths",":","Input","lengths","within","batch",".","Returns",":","Tensor",":","Output","with","dimensions","(","B",",","T",",","D",")",".","Tensor",":","Output","lengths","within","batch","."]
code: def forward(
      self, input: torch.Tensor, input_lengths: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """Apply a sliding window on the input.
  
      Args:
          input: Input (B, T) or (B, T,D), with D.
          input_lengths: Input lengths within batch.
  
      Returns:
          Tensor: Output with dimensions (B, T, D).
          Tensor: Output lengths within batch.
      """
      x = self.embed(input)
  
      return x, input_lengths
  
file_path: espnet/espnet2/mt/frontend/embedding.py
imports: from typing import Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
label_desc: Use image node_id 2 for calling the Embedding obj's underlying member method code with example usage: obj.forward(input, input_lengths) and returns: x,  input_lengths